Recognizing people in images is a task that is easy for humans but much harder for computers. Being capable of recognizing a substantial number of individuals with high precision and high recall is of great value to many practical applications, such as surveillance, security, photo tagging, and celebrity recognition.
Building a large-scale face recognizer is a non-trivial effort. One challenge is to recognize people when there are few training samples, maybe even just one sample for some people. This challenge naturally exists in many real scenarios, especially when the number of persons to be recognized is very large. Although recent years have witnessed great progress in deep learning and visual recognition, computer vision systems still lack the capability of learning visual concepts from just one, or very few, examples.